Intelligent fault prediction method and system for electric vehicle charging pile

Abstract

The invention discloses an intelligent fault prediction method and system for an electric vehicle charging pile, and relates to the technical field of intelligent fault prediction of charging piles, and the method comprises the following steps: carrying out the quantification of the sensitivity of historical magnetic flux detection through the output of a Hall sensor; taking the outputs of a plurality of different Hall sensors as a plurality of magnetic flux detection sensitivities; obtaining interference data of current load change on the magnetic field and detection quality data; based on a bp neural network algorithm, constructing a Hall sensor output evaluation model; enabling the output of the Hall sensor output evaluation model to correspond to the output of the corresponding Hall sensor, constructing a two-dimensional display model, carrying out the data analysis of the two-dimensional display model, obtaining the improved Hall sensor output, and enabling the improved Hall sensor output to serve as the magnetic flux detection sensitivity to be applied to the fault prediction of the cable of the charging pile. The problem that the accuracy and the false alarm rate of fault detection cannot be effectively balanced in cable fault detection through magnetic flux in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent fault prediction of charging piles, and more specifically, to an intelligent fault prediction method and system for electric vehicle charging piles. Background Art

[0002] With the popularity of electric vehicles, charging piles, as a key part of charging infrastructure, are becoming more and more important in terms of operational stability and fault prediction. However, since the charging pile system is complex and includes multiple components such as power transmission, cable connection, and charging guns, and these components need to operate stably for a long time, fault prediction and detection of charging piles is particularly important.

[0003] Modern charging pile intelligent fault prediction technology mainly realizes early identification of faults through real-time collection and analysis of multiple sensor data. For example, in the characteristic parameters of charging equipment, the state of the characteristic parameters of power supply equipment and auxiliary power supply equipment can be monitored through voltage collection and voltage detection circuits, and the possible failure risks of equipment characteristic parameters can be identified by analyzing voltage fluctuations and abnormal conditions. For charging guns and gun holders, the state sensor or resistance change can be used to determine whether the charging gun is in a normal connection state, thereby realizing real-time detection of the interface.

[0004] For charging pile cables, intelligent fault prediction technology can also use magnetic flux detection and image analysis technology to perform multi-level detection of cable surface defects and internal structures. For example, magnetic flux detection can be used to identify whether there are abnormal magnetic field changes in the cable and determine whether there are physical defects. At the same time, combined with image recognition technology, defect detection on the cable surface or cross-sectional images can help quickly locate the fault site and assess its severity. This intelligent detection method can not only improve the accuracy of fault detection, but also promptly discover and prevent potential safety hazards.

[0005] For example, the invention patent with announcement number: CN114113833B announces a charging pile and a method and device for detecting characteristic parameters of charging pile equipment, including a charger circuit, including: a charging circuit, a first voltage acquisition circuit connected to the characteristic parameters of the DC power supply equipment, a second voltage acquisition circuit connected to the characteristic parameters of the auxiliary power supply equipment, and a first voltage detection circuit connected to the characteristic parameter CC1 of the first charging connection confirmation equipment; a charging gun connected to the charger circuit; a gun seat for placing the charging gun, including: a travel switch for detecting the placement state of the charging gun, an adjustable resistor connected between the equipment characteristic parameter CC1 and the ground wire, a third voltage acquisition circuit connected between the characteristic parameters of the DC power supply equipment and the characteristic parameters of the auxiliary power supply equipment, a second voltage detection circuit connected to the characteristic parameter CC2 of the second charging connection confirmation equipment, and a third voltage detection circuit connected to the ground wire; a controller connected to the charger circuit and the gun seat for controlling the charging circuit. This application solves the technical problems of the complicated fault detection process of the equipment characteristic parameters of the charging pile and the poor user experience in the related art.

[0006] For example, the invention patent with announcement number: CN115144704A announces a fault detection method, device, equipment and storage medium for cable production, including methods for improving the accuracy of fault detection in cable production and the safety of cables. The method includes: if the root cause analysis result is that the charging pile cable is abnormal, then the charging pile cable is subjected to magnetic flux detection to obtain a magnetic flux detection result; if there is a defect in the charging pile cable, multiple cable surface images corresponding to the charging pile cable are collected, and the charging pile cable is subjected to cable surface defect detection based on the multiple cable surface images to obtain a surface defect detection result; the charging pile cable is fault located based on the surface defect detection result to obtain a fault location result, and the faulty cable corresponding to the charging pile cable is intercepted based on the fault location result, and a cross-sectional image of the faulty cable is collected; the cross-sectional image is input into a preset cable fault detection model to perform cable fault detection to obtain a cable fault detection result.

[0007] The above disclosed technical solutions have at least the following technical problems: during the cable fault analysis process, there is a lack of effective adjustment mechanism for setting the cable's magnetic flux detection sensitivity, resulting in insufficient or excessive sensitivity adjustment in different application scenarios.

[0008] On the one hand, too low magnetic flux detection sensitivity may fail to capture weak fault signals in the cable, missing the opportunity for early fault diagnosis and affecting the timeliness of the early warning system; On the other hand, too high a magnetic flux detection sensitivity may result in an over-response to current fluctuations under normal working conditions, resulting in excessive false alarms or noise interference, thereby reducing the reliability and accuracy of the system.

[0009] In the existing cable fault detection by magnetic flux, it is impossible to effectively balance the accuracy of fault detection and the false alarm rate, which affects the effect of cable fault diagnosis and user experience. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0010] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an intelligent fault prediction method and system for an electric vehicle charging pile, which continuously adjusts the gain of the Hall sensor to analyze the fault detection effect under different magnetic flux detection sensitivities, so as to solve the problem that the accuracy and false alarm rate of fault detection cannot be effectively balanced in the existing cable fault detection by magnetic flux, thus affecting the effect of cable fault diagnosis and user experience.

[0011] To achieve the above object, the present invention provides the following technical solutions: An intelligent fault prediction method for an electric vehicle charging pile comprises the following steps: performing magnetic flux detection on a charging pile cable, and if the magnetic flux detection result indicates that the charging pile cable has a hidden danger, collecting a cable surface image, locating the fault, and analyzing a cross-sectional image of the cable at the located fault position, thereby predicting the fault; quantifying the sensitivity during historical magnetic flux detection through the output of a Hall sensor, and averaging to obtain an initial magnetic flux detection sensitivity; performing random Hall sensor gain several times under the initial magnetic flux detection sensitivity, and obtaining outputs of several different Hall sensors as several magnetic flux detection sensitivities; obtaining interference data and detection quality data of current load changes on the magnetic field for charging pile cable fault prediction under several magnetic flux detection sensitivities; constructing a Hall sensor output evaluation model based on the BP neural network algorithm according to the interference data and detection quality data of current load changes on the magnetic field; making the output of the Hall sensor output evaluation model correspond to the output of the corresponding Hall sensor, constructing a two-dimensional display model, and performing data analysis on the two-dimensional display model to obtain an improved Hall sensor output, and applying it as the magnetic flux detection sensitivity to charging pile cable fault prediction.

[0012] In a preferred embodiment, the magnetic flux detection of the charging pile cable is specifically as follows: using a Hall sensor to perform real-time detection of the magnetic field around the charging pile cable to obtain magnetic flux data of the cable in a working state; comparing the collected magnetic flux data with a reference magnetic flux under normal working conditions to identify deviations in the magnetic field; analyzing the amplitude and frequency of magnetic flux changes, constructing a change curve, analyzing sudden changes and periodic fluctuations in the change curve, and comparing them with a preset safety range. When the preset safety range is exceeded, it indicates that there are hidden dangers in the charging pile cable.

[0013] In a preferred embodiment, the sensitivity of historical magnetic flux detection is quantified through the output of the Hall sensor, and the initial magnetic flux detection sensitivity is obtained by averaging, specifically: the historical magnetic flux data of the Hall sensor at different time points are obtained from the historical records; the historical magnetic flux data at different time points are quantified by calculating the change in the magnetic flux value of each data point per unit time; the change in unit time of all historical magnetic flux detection data is averaged as the initial magnetic flux detection sensitivity.

[0014] In a preferred embodiment, the Hall sensor gain is randomly performed several times under the initial magnetic flux detection sensitivity to obtain several different Hall sensor outputs as several magnetic flux detection sensitivities, specifically: according to historical data, the gain range of the Hall sensor is obtained, and several gain values ​​are randomly obtained within the gain range of the Hall sensor based on computer random numbers; the initial magnetic flux detection sensitivity is disturbed by several gain values ​​to obtain several different Hall sensor outputs as several magnetic flux detection sensitivities.

[0015] In a preferred embodiment, the detection quality data includes a detection response fluctuation coefficient, and a specific method for obtaining the detection response fluctuation coefficient is as follows: obtain several signal fluctuation amplitudes during the charging pile cable magnetic flux detection; the signal fluctuation amplitude standard deviation and the signal fluctuation amplitude average value of the signal fluctuation amplitudes at the same time interval during the charging pile cable magnetic flux detection; calculate the signal fluctuation amplitude variation coefficient based on the signal fluctuation amplitude standard deviation and the signal fluctuation amplitude average value; calculate the detection response fluctuation coefficient based on the signal fluctuation amplitude variation coefficient based on a preset detection response fluctuation coefficient calculation formula.

[0016] In a preferred embodiment, the interference data of the current load change on the magnetic field includes the current load influence coefficient and the magnetic flux misdetection anomaly coefficient. The specific method for obtaining the current load influence coefficient is as follows: obtain the waveforms of the magnetic induction intensity vector and the current load signal changing with time; obtain the harmonic composition of the magnetic induction intensity vector signal waveform and the current load signal waveform at a preset frequency, respectively, and perform harmonic component analysis to obtain the basic waveform component; convert the basic waveform component into a harmonic component based on an inverse Fourier transform; and calculate the floating value of the harmonic based on the quotient of the harmonic component and the basic waveform component; perform data analysis on the floating value of the harmonic to calculate the current load influence coefficient.

[0017] In a preferred embodiment, the specific method for obtaining the magnetic flux misdetection anomaly coefficient is as follows: according to the device characteristic parameters obtained in the charging pile system, multiply by the corresponding influence coefficient to obtain the actual electromagnetic interference influence, the device characteristic parameters include current, power, and frequency; according to the device characteristic parameters, combined with the preset electromagnetic interference source calculation formula, calculate the actual electromagnetic interference influence of each device characteristic parameter; combine the preset interference frequency calculation formula to calculate the electromagnetic interference influence of different frequency bands; according to historical monitoring data, determine the standard electromagnetic interference influence of each type of equipment characteristic parameters; compare the actual electromagnetic interference influence with the standard electromagnetic interference influence and add them to obtain the magnetic flux misdetection anomaly coefficient.

[0018] In a preferred embodiment, the output of the Hall sensor output evaluation model is corresponded to the output of the corresponding Hall sensor to construct a two-dimensional display model, specifically: a two-dimensional array is constructed by the output of the Hall sensor output evaluation model and the corresponding magnetic flux detection sensitivity; the magnetic flux detection sensitivities of several two-dimensional arrays are arranged from large to small as the horizontal axis, and the output of the corresponding Hall sensor output evaluation model is used as the vertical axis to construct a two-dimensional coordinate system in which the output of the Hall sensor output evaluation model changes with the magnetic flux detection sensitivity; several two-dimensional arrays are marked on the two-dimensional coordinate system and connected with smooth curves to construct a two-dimensional display model.

[0019] In a preferred embodiment, the two-dimensional display model is subjected to data analysis to obtain an improved Hall sensor output, and the output is used as a magnetic flux detection sensitivity for charging pile cable fault prediction. Specifically, the magnetic flux detection sensitivity corresponding to several peak values ​​is obtained according to the two-dimensional display model; the second-order derivatives of several peak values ​​are calculated to determine the rate at which the output of the Hall sensor output evaluation model changes with the magnetic flux detection sensitivity, and the magnetic flux detection sensitivity corresponding to the peak value with the smallest absolute value of the second-order derivative is used as the output of the improved Hall sensor.

[0020] The technical effects and advantages of the intelligent fault prediction method and system for electric vehicle charging piles of the present invention are as follows: 1. The present invention realizes intelligent fault prediction and hidden danger monitoring of charging pile cables by combining magnetic flux detection, image acquisition and fault location. First, the system uses magnetic flux detection to identify potential cable problems, and collects cable surface images and cross-sectional images to locate and analyze faults, ensuring accurate prediction of the fault location. Secondly, in view of the sensitivity of the Hall sensor, the detection accuracy of the sensor is further optimized by quantifying historical detection data and adjusting the gain. By analyzing the data of the interference of current load changes on the magnetic field under different sensitivities, a Hall sensor output evaluation model is constructed based on the BP neural network algorithm, which effectively improves the accuracy and robustness of fault prediction. This method not only improves the accuracy and reliability of charging pile cable fault detection, but also can perform efficient fault diagnosis in complex environments. It has significant advantages such as intelligence, strong adaptability and strong anti-interference ability.

[0021] 2. The present invention can effectively evaluate and improve the output performance of the Hall sensor by constructing a Hall sensor output evaluation model based on the BP neural network algorithm. By considering multiple factors such as current load, magnetic field interference and detection quality, the Hall sensor output evaluation coefficient is generated, and the data is deeply analyzed through a two-dimensional display model to find the optimal magnetic flux detection sensitivity. This method can improve the stability and accuracy of the Hall sensor under dynamic loads and complex environments. Especially in the application of charging pile cable fault prediction, the improved sensor output can provide higher sensitivity and more accurate fault diagnosis, thereby improving the reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a structural schematic diagram of an intelligent fault prediction method for an electric vehicle charging pile according to the present invention.

[0023] Figure 2 The present invention is a structural schematic diagram of an intelligent fault prediction system for an electric vehicle charging pile. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Embodiment 1, Figure 1 The present invention provides an intelligent fault prediction method for an electric vehicle charging pile, comprising: S1, perform magnetic flux detection on the charging pile cable. If the magnetic flux detection result shows that the charging pile cable has hidden dangers, collect the cable surface image, locate the fault and analyze the cross-sectional image of the cable at the located fault position, so as to predict the fault.

[0026] The magnetic flux detection of the charging pile cable is specifically performed as follows: Use Hall sensors to detect the magnetic field around the charging pile cable in real time and obtain the magnetic flux data of the cable in working state; Compare the collected magnetic flux data with the reference magnetic flux under normal working conditions to identify the deviation of the magnetic field; Analyze the amplitude and frequency of magnetic flux changes, construct a change curve, analyze the sudden changes and periodic fluctuations of the change curve, and compare it with the preset safety range. When it exceeds the preset safety range, it indicates that there are hidden dangers in the charging pile cable.

[0027] It should be noted that under normal working conditions, the magnetic flux data of the charging pile cable will be collected and recorded in advance as a reference value. By comparing the currently collected magnetic flux data with the reference magnetic flux, it is possible to identify whether there is a deviation in the current magnetic field. The existence of deviation may mean that there is an abnormal current distribution or fault phenomenon around the cable. Perform time series analysis on the magnetic flux data to construct a magnetic flux change curve. By analyzing the fluctuation pattern of the curve, the amplitude and frequency characteristics of the magnetic flux change are identified. If the amplitude of the magnetic flux change increases suddenly or shows abnormal periodic fluctuations, it may indicate that the cable is faulty or potentially damaged. By comparing the magnetic flux change curve with the preset safety range (such as maximum fluctuation amplitude, frequency range, etc.), it is determined whether there are hidden dangers. When the magnetic flux change exceeds the safety range, the system will issue an alarm to indicate that the cable has a possible risk of failure or damage and requires further inspection or maintenance.

[0028] If the magnetic flux detection result indicates that there is a hidden danger in the charging pile cable, the cable surface image is collected to locate the fault and the cross-sectional image of the cable at the located fault position is analyzed to predict the fault, specifically: If the magnetic flux detection results indicate that the charging pile cable has hidden dangers, first collect images of the cable surface. Use high-precision camera equipment to obtain image data of the cable surface in order to further analyze potential surface defects or damage; The collected cable surface images are analyzed through image processing technology to detect whether there are cracks, burn marks, discoloration or other visible damage on the surface. These surface defects may be the external manifestation of cable failure, indicating that there may be deeper problems; According to the results of surface defect detection, the cable fault area is located. The specific location of the cable surface abnormality is determined, and accurate positioning data is provided for the next step of fault analysis.

[0029] After locating the fault area, a cross-sectional image of the cable at the fault location is acquired. The cross-sectional image can provide detailed information on the inside of the cable, such as insulation damage, conductor corrosion, or other structural problems.

[0030] The cross-sectional images are input into the pre-set cable fault detection model for further analysis to determine the type, location and severity of the fault. The model is used to evaluate the possible causes of the fault and predict the fault development trend, ultimately providing a basis for repair and maintenance decisions.

[0031] It should be noted that there are mature technologies for fault prediction using image analysis, which will not be elaborated here.

[0032] S2, quantify the historical magnetic flux detection sensitivity through the output of the Hall sensor, and average it to obtain the initial magnetic flux detection sensitivity.

[0033] The sensitivity of the historical magnetic flux detection is quantified by the output of the Hall sensor, and the initial magnetic flux detection sensitivity is obtained by averaging, which is specifically: Get the historical magnetic flux data of the Hall sensor at different time points from the historical record.

[0034] By calculating the change in the magnetic flux value per unit time of each data point, the historical magnetic flux data at different time points are quantified; The average change per unit time of all historical magnetic flux detection data is calculated as the initial magnetic flux detection sensitivity.

[0035] It should be noted that the magnetic flux data of the Hall sensor at different time points covers the changes in the magnetic field of the cable under various working conditions, including normal operation and possible fault conditions. If necessary, sensitivity calibration and correction can be performed based on the magnetic flux data under known faults or abnormal cable conditions to ensure that the initial sensitivity value obtained is highly representative and reliable.

[0036] S3, performing random Hall sensor gains several times at the initial magnetic flux detection sensitivity to obtain outputs of several different Hall sensors as several magnetic flux detection sensitivities.

[0037] The Hall sensor gain is randomly performed several times under the initial magnetic flux detection sensitivity to obtain outputs of several different Hall sensors as several magnetic flux detection sensitivities, specifically: According to historical data, the gain range of the Hall sensor is obtained, and a number of gain values ​​are randomly obtained within the gain range of the Hall sensor based on a computer random number; The initial magnetic flux detection sensitivity is disturbed by a plurality of gain values ​​to obtain outputs of a plurality of different Hall sensors as a plurality of magnetic flux detection sensitivities.

[0038] The above method obtains the gain range of the Hall sensor based on historical data, and uses computer random numbers to generate several gain values ​​for disturbance, thereby obtaining multiple different Hall sensor outputs. The advantage of this method is that by introducing randomness, it can fully cover the magnetic flux detection sensitivity under different gain configurations, avoid the limitations that may be brought about by a single gain value, and provide a richer output data set. These diverse output data can help analyze the fault detection performance under different gains, thereby providing a more comprehensive basis for optimizing the Hall sensor gain selection, helping to improve the sensitivity and accuracy of the system and reduce the risk of false alarms and missed alarms.

[0039] S4, obtaining interference data and detection quality data of the current load change on the magnetic field for charging pile cable fault prediction under several magnetic flux detection sensitivities.

[0040] The interference data of the current load change on the magnetic field includes the current load influence coefficient and the magnetic flux misdetection anomaly coefficient; The detection quality data includes a detection response fluctuation coefficient.

[0041] The current load influence coefficient is used to quantify the impact of the interference of current load changes on the magnetic field on the fault detection results, especially the impact on the false alarm rate. Since the current load fluctuations of the charging pile during operation may introduce additional electromagnetic interference, these interference signals may be misjudged by the Hall sensor as cable faults, resulting in false alarms. The current load influence coefficient helps evaluate the degree of excessive response of the system to noise under different sensitivity settings by measuring the correlation between the detection error of the magnetic flux and the actual fault under different current loads. This coefficient helps to optimize the sensitivity of magnetic flux detection to reduce false alarms caused by current load fluctuations, thereby improving the accuracy and reliability of fault prediction.

[0042] The fault prediction effect of the current load influence coefficient under different magnetic flux detection sensitivities is analyzed to solve the problem that the accuracy and false alarm rate of fault detection cannot be effectively balanced in the existing cable fault detection by magnetic flux. The advantages are as follows: Optimize sensitivity settings: By quantifying the impact of current load changes on magnetic field disturbances, the current load impact factor can help identify the over-response of the flux detection system to electrical ambient noise at different sensitivities, thereby adjusting the sensitivity of the Hall sensor and optimizing the system's performance in the face of current load fluctuations.

[0043] Improve fault diagnosis accuracy: This coefficient can help distinguish real faults from false fault signals caused by current load, reduce false alarms, improve the accuracy of fault detection, and ensure more reliable cable fault prediction.

[0044] Reduce false alarm rate: Fluctuations in current load may cause interference signals from the magnetic field to be misjudged as faults. The current load influence coefficient can effectively prevent the system from over-responding to interference signals by evaluating the false alarm rate at different sensitivities, reducing the occurrence of false alarms and thus improving user experience.

[0045] Enhanced system stability: By precisely adjusting the magnetic flux sensitivity and combining the evaluation of the current load influence coefficient, the stability of fault detection can be maintained under different working environments and load changes, avoiding detection instability caused by current load changes.

[0046] Optimize user experience: reduce false alarm rate and improve fault prediction accuracy, make charging pile maintenance and management more efficient, reduce unnecessary inspections and downtime, and improve user satisfaction of the overall system.

[0047] The specific method for obtaining the current load influence coefficient is as follows: Obtaining the waveforms of the magnetic induction intensity vector and the current load signal changing with time; Obtaining the harmonic composition of the magnetic induction intensity vector signal waveform and the current load signal waveform at a preset frequency, and performing harmonic component analysis to obtain a basic waveform component; The basic waveform component is converted into a harmonic component based on an inverse Fourier transform; and the floating value of the harmonic is calculated according to the quotient of the harmonic component and the basic waveform component; The floating values ​​of harmonics are analyzed and the current load influence coefficient is calculated.

[0048] Among them, the specific calculation formula of the current load influence coefficient is as follows:

[0049] In the formula, is the current load influence coefficient, is the number of harmonic components, is the current load data volume, z is the current load data volume, j is the number of harmonic components, is the inverse Fourier transform, is the magnetic induction intensity vector waveform, is the current load waveform, is the average value of magnetic induction intensity vector, is the average current load value.

[0050] The magnetic flux false detection anomaly coefficient is used to measure the impact of external electromagnetic interference on the output signal of the Hall sensor, especially the false alarms that may be caused under high sensitivity settings. Sources of electromagnetic interference include electromagnetic noise, lightning, etc. from other devices near the charging pile (such as air conditioners, power tools, motors, etc.). By monitoring the changes in the magnetic flux of the cable when these devices are working, the magnetic flux false detection anomaly coefficient can analyze the interference of external electromagnetic noise on the magnetic field and help evaluate how the Hall sensor responds to these interference signals under different sensitivity settings. High sensitivity settings may produce excessive signal fluctuations to external noise, resulting in an increase in false alarm rates, while low sensitivity may not accurately capture interference signals, thereby affecting the accuracy of fault detection. This coefficient provides a basis for sensitivity optimization, ensuring that false alarms are reduced in high interference environments and improving the reliability of fault prediction.

[0051] The fault prediction effect of the magnetic flux misdetection anomaly coefficient under different magnetic flux detection sensitivities is analyzed to solve the problem that the accuracy and false alarm rate of fault detection cannot be effectively balanced in the existing cable fault detection by magnetic flux. The advantages are as follows: Optimize sensitivity settings: By quantifying the impact of electromagnetic noise on the Hall sensor, it can help adjust the sensitivity setting to reduce the response to environmental noise while still maintaining sensitivity to actual fault signals, thereby improving the accuracy of fault prediction.

[0052] Reduce false alarms: The flux false detection anomaly coefficient can reveal excessive responses to external electromagnetic interference at high sensitivity settings, helping the system avoid unnecessary false alarms when current load changes or external devices are operating, ensuring a more accurate fault detection system.

[0053] Improve anti-interference ability: By monitoring the impact of electromagnetic noise on detection results at different sensitivities, it is possible to identify and reduce false alarms caused by electromagnetic interference (such as from other devices near the charging pile or natural factors such as lightning), thereby improving the stability and reliability of the system in high-interference environments.

[0054] Enhanced fault detection reliability: By effectively identifying and filtering interference signals, the magnetic flux false detection anomaly coefficient helps the system balance the sensitivity and accuracy of detection in cable fault detection, ensuring that accurate fault prediction results can still be provided in complex electrical environments.

[0055] The specific method for obtaining the magnetic flux misdetection anomaly coefficient is as follows: The specific method for obtaining the magnetic flux misdetection anomaly coefficient is as follows: The actual electromagnetic interference impact is obtained by multiplying the device characteristic parameters obtained in the charging pile system by the corresponding influence coefficient, wherein the device characteristic parameters include current, power, and frequency; According to the equipment characteristic parameters, the actual electromagnetic interference impact of each equipment characteristic parameter is calculated in combination with the preset electromagnetic interference source calculation formula; Combine the preset interference frequency calculation formula to calculate the electromagnetic interference impact of different frequency bands; Determine the standard electromagnetic interference impact of characteristic parameters of various equipment based on historical monitoring data; Compare and add the actual electromagnetic interference effect with the standard electromagnetic interference effect to obtain the magnetic flux misdetection anomaly coefficient; The specific calculation formula for the actual electromagnetic interference impact of the characteristic parameters of each device is as follows:

[0056] The specific calculation formula for the electromagnetic interference impact of different frequency bands is as follows:

[0057] The specific calculation formula of the magnetic flux misdetection anomaly coefficient is as follows:

[0058] In the formula, is the actual electromagnetic interference impact of each device characteristic parameter, m is the number of device characteristic parameters, is the topological distance of the device characteristic parameters, is power, is the frequency, is the current, For the electromagnetic interference influence of different frequency bands, is the total number of preset interference frequencies, is the interference frequency, is the difference between the upper and lower limits of the interference frequency, is the frequency response of the interference frequency, is the bandwidth of the interference frequency; is the magnetic flux misdetection anomaly coefficient, The electromagnetic interference impact of each device characteristic parameter standard, Standard electromagnetic interference impact for different frequency bands.

[0059] The detection response fluctuation coefficient is used to quantify the response of different fault types (such as short circuit, open circuit, poor contact, etc.) to magnetic field changes under different sensitivity settings. By analyzing the fluctuation of magnetic flux when a fault occurs, it is possible to determine whether the sensitivity setting can effectively capture the characteristic signals of different fault types. For short circuit faults, strong magnetic field changes are usually generated. High sensitivity can respond faster, but too high sensitivity may cause excessive signal fluctuation amplitude for normal fluctuations; for poor contact faults, low sensitivity may not be able to capture subtle current fluctuations, and high sensitivity may be misjudged as a fault. Therefore, the detection response fluctuation coefficient helps evaluate the rationality of the sensitivity setting, ensures that the system can distinguish between fault signals and normal working conditions, and improves the accuracy and reliability of fault diagnosis.

[0060] The fault prediction effect of the current load influence coefficient under different magnetic flux detection sensitivities is analyzed to solve the problem that the accuracy and false alarm rate of fault detection cannot be effectively balanced in the existing cable fault detection by magnetic flux. The advantages are as follows: Reduce false alarm responses to current load fluctuations: By analyzing the interference of current load changes on the magnetic field, the sensitivity setting can be optimized to avoid excessive response to normal current load fluctuations at high sensitivity, thereby reducing the false alarm rate. High load current changes usually cause changes in the magnetic field, but too high a sensitivity may mistakenly judge these changes as fault signals. When adjusting the sensitivity, setting the current load influence coefficient reasonably can effectively avoid this situation.

[0061] Improve the accuracy of fault prediction: The current load impact coefficient can be used to evaluate the sensitivity of fault prediction under different current load conditions, thereby more accurately distinguishing between faults and normal working conditions. For example, when the current load is large, a low sensitivity setting may not respond to the fault signal in time, while too high a sensitivity may introduce unnecessary false alarms. By balancing this coefficient, it can be ensured that faults are identified promptly and accurately when they occur.

[0062] Enhance the system's ability to resist interference signals: Current load changes are often accompanied by the switching of electrical equipment, power fluctuations, and other phenomena, which can interfere with the magnetic field detection of the Hall sensor. By reasonably calculating the current load influence coefficient, the system's ability to resist external electromagnetic interference can be improved while ensuring normal fault detection, and the output signal of the Hall sensor can be optimized.

[0063] Optimize the balance between sensitivity adjustment and false alarm rate: The current load influence coefficient can provide a quantitative basis for sensitivity setting, helping the system to find the optimal sensitivity setting point under different current loads, thereby avoiding false alarms or missed alarms caused by too high or too low sensitivity. This can ensure timely identification of faults under high loads and avoid incorrect judgments of irrelevant interference signals.

[0064] Improve the reliability of cable fault diagnosis: By accurately analyzing the relationship between the change of current load and magnetic field interference, the fault prediction algorithm can be further optimized so that the system can maintain high accuracy and reliability under different working environments and load conditions. By optimizing the current load influence coefficient, external factors can be effectively distinguished from fault signals, thereby improving the credibility of fault prediction.

[0065] The combined effect of these advantages can effectively solve the problem of how to find a reasonable balance between accuracy and false alarm rate in charging pile cable fault detection, thereby improving fault detection performance and user experience.

[0066] The specific method for obtaining the detection response fluctuation coefficient is as follows: Obtain the signal fluctuation amplitudes of several charging pile cable magnetic flux detections; The standard deviation of the signal fluctuation amplitude and the average value of the signal fluctuation amplitude at the same time interval during the magnetic flux detection of the charging pile cable; Calculate the coefficient of variation of signal fluctuation amplitude according to the standard deviation of signal fluctuation amplitude and the average value of signal fluctuation amplitude; The signal fluctuation amplitude variation coefficient is calculated based on a preset detection response fluctuation coefficient calculation formula to calculate the detection response fluctuation coefficient.

[0067] The specific calculation formula of the signal fluctuation amplitude variation coefficient is as follows:

[0068] The specific calculation formula of the detection response fluctuation coefficient is as follows:

[0069] In the formula, is the coefficient of variation of signal fluctuation amplitude, is the signal fluctuation amplitude at the jth moment, is the total number of moments collected during the magnetic flux detection of the charging pile cable, and j is the moment number; is the detection response fluctuation coefficient.

[0070] This embodiment realizes intelligent fault prediction and hidden danger monitoring of charging pile cables by combining magnetic flux detection, image acquisition and fault location. First, the system uses magnetic flux detection to identify potential cable problems, and collects cable surface images and cross-sectional images to locate and analyze faults, ensuring accurate prediction of the location of the fault. Secondly, in view of the sensitivity of the Hall sensor, the detection accuracy of the sensor is further optimized by quantifying historical detection data and adjusting the gain. By analyzing the data of the interference of current load changes on the magnetic field under different sensitivities, a Hall sensor output evaluation model is constructed based on the BP neural network algorithm, which effectively improves the accuracy and robustness of fault prediction. This method not only improves the accuracy and reliability of charging pile cable fault detection, but also can perform efficient fault diagnosis in complex environments. It has significant advantages such as intelligence, strong adaptability and strong anti-interference ability.

[0071] Embodiment 2, S5, constructs a Hall sensor output evaluation model based on the BP neural network algorithm according to the interference data of the current load change on the magnetic field and the detection quality data.

[0072] The Hall sensor output evaluation model is constructed based on the BP neural network algorithm according to the interference data of the current load change on the magnetic field and the detection quality data, specifically: The obtained current load influence coefficient, magnetic flux misdetection abnormality coefficient and detection response fluctuation coefficient are used to construct a Hall sensor output evaluation model to generate a Hall sensor output evaluation coefficient; The specific calculation formula of the Hall sensor output evaluation coefficient is as follows:

[0073] In the formula, is the Hall sensor output evaluation coefficient, is the preset proportionality factor of the current load influence factor, is the preset proportional coefficient of the magnetic flux misdetection anomaly coefficient, The preset proportionality coefficient for the detection response fluctuation coefficient, is the current load influence coefficient, is the magnetic flux misdetection anomaly coefficient, is the detection response fluctuation coefficient.

[0074] S6, the output of the Hall sensor output evaluation model is matched with the output of the corresponding Hall sensor, a two-dimensional display model is constructed, and data analysis is performed on the two-dimensional display model to obtain an improved Hall sensor output, which is used as a magnetic flux detection sensitivity for charging pile cable fault prediction.

[0075] The output of the Hall sensor output evaluation model is matched with the output of the corresponding Hall sensor to construct a two-dimensional display model, specifically: The output of the Hall sensor output evaluation model and the corresponding magnetic flux detection sensitivity are used to construct a two-dimensional array; Arrange the magnetic flux detection sensitivities of several two-dimensional arrays from large to small as the horizontal axis, and the output of the corresponding Hall sensor output evaluation model as the vertical axis, and construct a two-dimensional coordinate system in which the output of the Hall sensor output evaluation model changes with the magnetic flux detection sensitivity; Mark several two-dimensional arrays onto a two-dimensional coordinate system and connect them with smooth curves to construct a two-dimensional display model.

[0076] The two-dimensional display model is analyzed for data to obtain an improved Hall sensor output, which is used as the magnetic flux detection sensitivity for charging pile cable fault prediction, specifically: According to the two-dimensional display model, the magnetic flux detection sensitivity corresponding to several peak values ​​is obtained; The second-order derivatives of several peak values ​​are calculated to determine the rate at which the output of the Hall sensor output evaluation model changes with the flux detection sensitivity, and the flux detection sensitivity corresponding to the peak value with the smallest absolute value of the second-order derivative is used as the output of the improved Hall sensor.

[0077] This embodiment can effectively evaluate and improve the output performance of the Hall sensor by constructing a Hall sensor output evaluation model based on the BP neural network algorithm. By considering multiple factors such as current load, magnetic field interference and detection quality, the Hall sensor output evaluation coefficient is generated, and the data is deeply analyzed through a two-dimensional display model to find the optimal magnetic flux detection sensitivity. This method can improve the stability and accuracy of the Hall sensor under dynamic loads and complex environments. Especially in the application of charging pile cable fault prediction, the improved sensor output can provide higher sensitivity and more accurate fault diagnosis, thereby improving the reliability of the system.

[0078] Embodiment 3, Figure 2 An intelligent fault prediction system for an electric vehicle charging pile, comprising a fault prediction module, a magnetic flux detection sensitivity quantification module, a magnetic flux detection sensitivity acquisition module, a data analysis module, a data processing module and a magnetic flux detection sensitivity module; The fault prediction module is used to perform magnetic flux detection on the charging pile cable. If the magnetic flux detection result shows that the charging pile cable has hidden dangers, the cable surface image is collected to locate the fault and analyze the cross-sectional image of the cable at the located fault position, so as to predict the fault; The magnetic flux detection sensitivity quantification module is used to quantify the sensitivity of the historical magnetic flux detection through the output of the Hall sensor, and average the initial magnetic flux detection sensitivity; A magnetic flux detection sensitivity acquisition module is used to perform random Hall sensor gains several times under the initial magnetic flux detection sensitivity to obtain outputs of several different Hall sensors as several magnetic flux detection sensitivities; A data analysis module is used to obtain interference data and detection quality data of the current load change on the magnetic field for charging pile cable fault prediction under several magnetic flux detection sensitivities; The data processing module is used to construct a Hall sensor output evaluation model based on the BP neural network algorithm according to the interference data of the current load change on the magnetic field and the detection quality data; The magnetic flux detection sensitivity module is used to match the output of the Hall sensor output evaluation model with the output of the corresponding Hall sensor, build a two-dimensional display model, and perform data analysis on the two-dimensional display model to obtain the improved Hall sensor output, which will be used as the magnetic flux detection sensitivity for charging pile cable fault prediction.

[0079] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0080] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0081] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0082] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0083] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0084] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent fault prediction method for an electric vehicle charging pile, characterized in that: The steps include: Perform magnetic flux detection on the charging pile cable. If the magnetic flux detection result shows that the charging pile cable has hidden dangers, collect the cable surface image, locate the fault and analyze the cross-sectional image of the cable at the located fault position to predict the fault. The sensitivity of the historical magnetic flux detection is quantified by the output of the Hall sensor, and the initial magnetic flux detection sensitivity is obtained by averaging; Performing random Hall sensor gains several times at the initial magnetic flux detection sensitivity to obtain outputs of several different Hall sensors as several magnetic flux detection sensitivities; Obtain interference data and detection quality data of current load changes on magnetic fields for charging pile cable fault prediction under several magnetic flux detection sensitivities; According to the interference data of current load change on magnetic field and detection quality data, a Hall sensor output evaluation model is constructed based on BP neural network algorithm; The output of the Hall sensor output evaluation model is matched with the output of the corresponding Hall sensor, a two-dimensional display model is constructed, and data analysis is performed on the two-dimensional display model to obtain the improved Hall sensor output, which is used as the magnetic flux detection sensitivity for charging pile cable fault prediction.

2. The intelligent fault prediction method for electric vehicle charging pile according to claim 1, characterized in that: The magnetic flux detection of the charging pile cable is specifically performed as follows: Use Hall sensors to detect the magnetic field around the charging pile cable in real time and obtain the magnetic flux data of the cable in working state; Compare the collected magnetic flux data with the reference magnetic flux under normal working conditions to identify the deviation of the magnetic field; Analyze the amplitude and frequency of magnetic flux changes, construct a change curve, analyze the sudden changes and periodic fluctuations of the change curve, and compare it with the preset safety range. When it exceeds the preset safety range, it indicates that there are hidden dangers in the charging pile cable.

3. The intelligent fault prediction method for electric vehicle charging pile according to claim 2 is characterized in that: The sensitivity of the historical magnetic flux detection is quantified by the output of the Hall sensor, and the initial magnetic flux detection sensitivity is obtained by averaging, which is specifically: Obtain historical magnetic flux data of the Hall sensor at different time points from historical records; By calculating the change in the magnetic flux value per unit time of each data point, the historical magnetic flux data at different time points are quantified; The average change per unit time of all historical magnetic flux detection data is calculated as the initial magnetic flux detection sensitivity.

4. The intelligent fault prediction method for electric vehicle charging pile according to claim 3 is characterized in that: The Hall sensor gain is randomly performed several times under the initial magnetic flux detection sensitivity to obtain outputs of several different Hall sensors as several magnetic flux detection sensitivities, specifically: According to historical data, the gain range of the Hall sensor is obtained, and a number of gain values ​​are randomly obtained within the gain range of the Hall sensor based on a computer random number; The initial magnetic flux detection sensitivity is disturbed by a plurality of gain values ​​to obtain outputs of a plurality of different Hall sensors as a plurality of magnetic flux detection sensitivities.

5. The intelligent fault prediction method for electric vehicle charging pile according to claim 4 is characterized in that: The detection quality data includes a detection response fluctuation coefficient, and a specific method for obtaining the detection response fluctuation coefficient is as follows: Obtain the signal fluctuation amplitudes of several charging pile cable magnetic flux detections; The standard deviation of the signal fluctuation amplitude and the average value of the signal fluctuation amplitude at the same time interval during the magnetic flux detection of the charging pile cable; Calculate the coefficient of variation of signal fluctuation amplitude according to the standard deviation of signal fluctuation amplitude and the average value of signal fluctuation amplitude; The signal fluctuation amplitude variation coefficient is calculated based on a preset detection response fluctuation coefficient calculation formula to calculate the detection response fluctuation coefficient.

6. The intelligent fault prediction method for electric vehicle charging pile according to claim 5, characterized in that: The interference data of the current load change on the magnetic field includes the current load influence coefficient and the magnetic flux misdetection anomaly coefficient. The specific method for obtaining the current load influence coefficient is as follows: Obtaining the waveforms of the magnetic induction intensity vector and the current load signal changing with time; Obtaining the harmonic composition of the magnetic induction intensity vector signal waveform and the current load signal waveform at a preset frequency, and performing harmonic component analysis to obtain a basic waveform component; The basic waveform component is converted into a harmonic component based on an inverse Fourier transform; and the floating value of the harmonic is calculated according to the quotient of the harmonic component and the basic waveform component; The floating values ​​of harmonics are analyzed and the current load influence coefficient is calculated.

7. The intelligent fault prediction method for electric vehicle charging pile according to claim 6, characterized in that: The specific method for obtaining the magnetic flux misdetection anomaly coefficient is as follows: The actual electromagnetic interference impact is obtained by multiplying the device characteristic parameters obtained in the charging pile system by the corresponding influence coefficient, wherein the device characteristic parameters include current, power, and frequency; According to the equipment characteristic parameters, the actual electromagnetic interference impact of each equipment characteristic parameter is calculated in combination with the preset electromagnetic interference source calculation formula; Combine the preset interference frequency calculation formula to calculate the electromagnetic interference impact of different frequency bands; Determine the standard electromagnetic interference impact of characteristic parameters of various equipment based on historical monitoring data; The actual electromagnetic interference effect is compared with the standard electromagnetic interference effect and added together to obtain the magnetic flux misdetection anomaly coefficient.

8. The intelligent fault prediction method for electric vehicle charging pile according to claim 7, characterized in that: The output of the Hall sensor output evaluation model is matched with the output of the corresponding Hall sensor to construct a two-dimensional display model, specifically: The output of the Hall sensor output evaluation model and the corresponding magnetic flux detection sensitivity are used to construct a two-dimensional array; Arrange the magnetic flux detection sensitivities of several two-dimensional arrays from large to small as the horizontal axis, and the output of the corresponding Hall sensor output evaluation model as the vertical axis, and construct a two-dimensional coordinate system in which the output of the Hall sensor output evaluation model changes with the magnetic flux detection sensitivity; Mark several two-dimensional arrays onto a two-dimensional coordinate system and connect them with smooth curves to construct a two-dimensional display model.

9. The intelligent fault prediction method for electric vehicle charging pile according to claim 8, characterized in that: The two-dimensional display model is analyzed for data to obtain an improved Hall sensor output, which is used as the magnetic flux detection sensitivity for charging pile cable fault prediction, specifically: According to the two-dimensional display model, the magnetic flux detection sensitivity corresponding to several peak values ​​is obtained; The second-order derivatives of several peak values ​​are calculated to determine the rate at which the output of the Hall sensor output evaluation model changes with the flux detection sensitivity, and the flux detection sensitivity corresponding to the peak value with the smallest absolute value of the second-order derivative is used as the output of the improved Hall sensor.

10. A system using the intelligent fault prediction method for an electric vehicle charging pile according to any one of claims 1 to 9, characterized in that: It includes a fault prediction module, a magnetic flux detection sensitivity quantification module, a magnetic flux detection sensitivity acquisition module, a data analysis module, a data processing module and a magnetic flux detection sensitivity module; The fault prediction module is used to perform magnetic flux detection on the charging pile cable. If the magnetic flux detection result shows that the charging pile cable has hidden dangers, the cable surface image is collected to locate the fault and analyze the cross-sectional image of the cable at the located fault position, so as to predict the fault; The magnetic flux detection sensitivity quantification module is used to quantify the sensitivity of the historical magnetic flux detection through the output of the Hall sensor, and average the initial magnetic flux detection sensitivity; A magnetic flux detection sensitivity acquisition module is used to perform random Hall sensor gains several times under the initial magnetic flux detection sensitivity to obtain outputs of several different Hall sensors as several magnetic flux detection sensitivities; A data analysis module is used to obtain interference data and detection quality data of the current load change on the magnetic field for charging pile cable fault prediction under several magnetic flux detection sensitivities; The data processing module is used to construct a Hall sensor output evaluation model based on the BP neural network algorithm according to the interference data of the current load change on the magnetic field and the detection quality data; The magnetic flux detection sensitivity module is used to match the output of the Hall sensor output evaluation model with the output of the corresponding Hall sensor, build a two-dimensional display model, and perform data analysis on the two-dimensional display model to obtain the improved Hall sensor output, which will be used as the magnetic flux detection sensitivity for charging pile cable fault prediction.

Images